Recommended prerequisite for participation in
The module builds on mathematical knowledge obtained in the
bachelor courses “Linear Algebra” and “Introduction to Probability
and Applied Statistics“ (bachelor in IT, Communication and New
Media), or similar.
Content, progress and pedagogy of the
Must have knowledge about:
- data modelling in form of preparing data, modelling data, and
evaluating and disseminating the results.
- key machine learning concepts such as feature extraction,
cross-validation, generalization and over-fitting, prediction and
curse of dimensionality.
- different machine learning principles, algorithms, techniques
and be able to define and describe fundamental problems and
consequences within machine learning.
- basic recommender system principles, techniques, algorithms and
be able to define and describe fundamental problems and
consequences within these.
Must be able to:
- discuss how the data modelling methods work and describe their
assumptions and limitations.
- map practical problems to standard data models such as
regression, classification, density estimation, clustering and
- select and apply a range of different machine learning
algorithms and techniques on specific problems.
- select and apply the basic recommender system algorithms and
techniques on specific problems
select and apply relevant machine learning algorithms and
techniques for detection of cyber attacks or anomalous behaviour in
Must have the competency to:
- solve machine learning related problems in a practical
- apply machine learning algorithms and analyse the
Type of instruction
Types of instruction are listed at the start of
§17; Structure and contents of the programme.
|Name of exam||Machine Learning|
|Type of exam|
Written or oral exam
|Assessment||7-point grading scale|
|Type of grading||Internal examination|
|Criteria of assessment||The criteria of assessment are stated in the Examination
Policies and Procedures|